Logistics Research Articles

基于改进 ACO-DWA 算法的轮式农业机器人仓库搬运避障路径研究

The existing obstacle avoidance control algorithms for wheeled agricultural warehouse handling robots are prone to the local optimal solution in the process of path optimization and collision can occur easily occur during multi-robot simultaneous operation. Given this, the obstacle avoidance of wheeled agricultural warehouse handling robots was explored in this study, and an obstacle avoidance path planning algorithm for wheeled agricultural handling robots in warehouse based on improved ACO-DWA algorithm was proposed. Then, the moving trajectory of agricultural warehouse handling robots during handling process was studied, and their spatial kinematics equation was given. Next, the real-time pose of agricultural warehouse handling robots was detected, and their motion path was planned considering the real-time position of obstacles and the target locations of handling. In addition, the obstacle avoidance controlling quantity of agricultural warehouse handling robots was calculated according to the deviation between the pose of robots and the planned path. Supported by a controller, the obstacle avoidance control work of agricultural warehouse handling robots was realized. It was concluded through the effect experiment that compared with the traditional method, the improved ACO-DWA algorithm designed in this study significantly reduced the number of collisions between agricultural robots, and through practical application, the proposed algorithm can meet the needs of improving warehouse logistics management efficiency.

Reaching Forest Workers with Yellow Fever Vaccine Through Engagement of the Private Sector in Central African Republic

Background/Objectives: Yellow fever (YF) outbreaks continue to affect populations that are not reached by routine immunization services, such as workers at a high risk of occupational exposure to YF. In the Central African Republic (CAR), YF cases were detected in districts characterized by the presence of workers in forest areas. We developed an innovative approach based on a local partnership with private companies of the extractive industry to administer YF vaccine to workers in remote areas during the response to an outbreak. Methods: The planning stage of the campaign included the mapping of forestry and mining companies through the involvement of national and/or local representatives of companies from both the formal and informal sectors. Information sessions and mobilization targeted the heads of operating companies. Advanced and mobile strategies were used to target workers on their work site. Companies provided logistical support including transportation and communication and set up temporary vaccination posts. Results: Using this local partnership, it was possible to vaccinate over 70,000 workers (5.8% of the entire vaccinated population) in hard-to-reach areas, protecting them from YF. This represented around 47% of the estimated number of workers and dependents. The partnership with the private sector also contributed to increasing knowledge on the risk of YF and means of protection among a high-risk community. Conclusions: Private companies represent potentially useful actors that can contribute to the protection of high-risk workers and to the prevention and control YF outbreaks. The experience in the CAR has demonstrated that it is possible to obtain support from private companies, including informal ones, for a vaccination campaign.

Predictive Analysis of Employee Turnover: A Comprehensive Model for Attrition Forecasting

Employee attrition has become a critical concern for organizations seeking to maintain a stable workforce and minimize turnover-related costs. This study aims to develop a predictive model to forecast employee attrition by analyzing key factors influencing turnover. The research investigates a range of variables, including push factors (e.g., job dissatisfaction, workplace conflict), pull factors (e.g., external job opportunities), organizational factors (e.g., leadership, culture), and external factors (e.g., economic conditions). The data is collected via a structured questionnaire through Google Forms, which captures responses related to these influencing factors.Using advanced machine learning techniques, this study evaluates multiple models, including logistic regression, decision trees, random forests, and support vector machines, to determine the most accurate model for predicting employee attrition. The evaluation is based on accuracy, precision, recall, and F1-score to ensure a robust prediction. The findings offer organizations valuable insights into the primary causes of employee turnover and provide a data- driven approach to proactively address these issues, reducing attrition rates and improving employee retention strategies. This research contributes to the existing body of knowledge by offering a comprehensive framework for predicting employee attrition, and its practical implications extend to human resource management, organizational development, and strategic planning. Keywords: Employee attrition, Turnover prediction, Predictive model, Push factors, Pull factors, Organizational factors, External factors, Machine learning, Logistic regression, Accuracy, Precision, Recall, F1-score, Employee retention, Human resource management, Workforce stability.

Determinants of E-commerce Adoption by MSMEs: An Empirical Analysis in Odisha

This article aims to examine the adoption of e-commerce by micro, small and medium enterprises (MSMEs) in Odisha. The quantitative survey approach was employed for the conduct of this research. A structured questionnaire was used to obtain the data from a sample of 100 representative MSMEs across the two administrative blocks in the Khordha district of Odisha. The relationship between the variables in this study was analysed by using binary logistic regression technique. The study found that most of the MSMEs had engaged in at least one form of e-commerce level, which is less than the desirable level. Further, the study provides an understanding of the factors responsible for e-commerce adoption by MSMEs in the district. The study found that three variables, that is, the number of staff with IT knowledge, interest to accept e-commerce and size of the business, are the major determining factors that influence MSMEs in adopting e-commerce. Significant challenges hindering e-commerce adoption among MSMEs in Khordha as identified in this study include problems with online banking, speed of the internet, lack of government support, problems in logistic support and lack of IT-qualified staff. Attention should continue to focus on improving the technical infrastructure needed to operate e-commerce and there is a need for media publicity to increase public awareness to push customers and employees to make use of e-commerce. The role of the government is also crucial in providing assistance and training that can help the use of e-commerce in the district.

Prediction of Disease Stage by Machine Learning Classification Methods for Covid-19 Patients

Supervised machine learning classificitaion algorithms have been widely used in many fields in recent years. Especially, health is one of the most important areas where machine learning studies are carried out successfully. The aim of this study is to develop models that predict the disease stage of people who apply to hospital with the diagnosis of Covid-19. Inadequacies such as intensive care occupancy, insufficiency of beds, and shortage of respiratory equipment are among these problems, and this has left healthcare workers faced with the overwhelming burden of patients. Therefore, estimating the disease stages of Covid-19 patients at an early stage is of great importance. The data set used in the study includes the clinical and laboratory data of the patients during in their admission to the hospital.It has been tried to develop models that predict disease stage by using Logistic Regression, Random Forest and Support Vector Machine algorithms in the data set. The random forest model with 9 variables was the best performing model. With the models obtained, it will be ensured that the hospital management receives information in order to see the necessary treatment for low-risk or high-risk patients and to avoid medical system inadequacies.

Study on Stock Price Fluctuation of DHL after Cross-border Merger and Acquisition

In the 21st century, global logistics companies have launched a huge wave of mergers and acquisitions (M&A), including many international logistics giants launching crazy M&A to adapt to the expansion strategy of globalization. The cross-border logistics market is an extremely attractive cake in the logistics market, but the express delivery business in many low tier cities in many countries has long been dominated by many local enterprises, and its market has not been fully developed and does not meet international standards. The world has experienced five waves of M&A, and now this trend is affecting the development of DHL and has a significant impact on the evolution of the logistics industry. This article introduces the background of the topic selection, research objectives and significance, as well as the examination of whether M&A are necessary. This article uses the ARIMA-GARCH model to predict DHL's future stock price, and confirms the relationship and impact between its stock price and M&A behavior by comparing actual data. Based on the company's stock liquidity, the M&A effect is evaluated. The research results indicate that M&A can enable companies to quickly acquire the resources needed for development, generate synergies through integration, establish core competitiveness, and gain competitive advantages.

Research on the Impact of Internet of Things (IoT) Technology on Logistics Warehouse Management

As a consequence of the evolution of the times, Internet of Things technology has made remarkable progress in the field of logistics and warehouse management. Its application promotes the intelligence of logistics management. This has the effect of markedly enhancing the efficiency of logistics management, thereby facilitating the accelerated growth of the logistics industry. This paper begins by examining the current state of logistics warehouse management and the fundamental principles of IoT technology and its application in the field of logistics. It then proceeds to discuss the challenges faced by traditional logistics warehouse management in China. This paper analyses how the application of IoT technology can significantly improve the efficiency of logistics management, and provides an in-depth discussion on the development trend of logistics warehouse management. The objective of this paper is to illustrate the pivotal role of IoT technology in logistics warehouse management and to highlight its beneficial influence on the prospective evolution of the logistics industry.

Improving Public Governance: The Relationship among Logistics Management, Transparency and Accountability in the Peruvian Public Sector

This research addressed deficiencies in logistics management and transparency in the Peruvian public sector, focusing specifically on the municipality studied. The research revealed critical problems in the supplier selection and contracting stages, as well as in the updating of the transparency portal, highlighting the erosion of integrity and fairness in public procurement. The study methodology combined quantitative approaches such as surveys and document analysis, which allowed for a comprehensive and nuanced understanding of the problem. Through data triangulation, detailed insights crucial to the development of effective interventions were obtained. The results highlight a significant correlation between effective logistics management practices and high levels of transparency and accountability. Specific practices within Group 3 are identified that could serve as a model for governance reform. This study not only addresses a gap in the existing literature, but also provides a solid foundation for the development of more effective and transparent public policies. By addressing the shortcomings of logistics management, the study contributed to the empowerment of the civil community and the creation of a more open and fair government, indirectly supporting the goals of environmental sustainability and conservation.

An Analysis of The Causes and Impacts of Corporate Mergers in The Logistics Industry: A Case Study of JD Logistics' Acquisition of Deppon Express

This article deeply analyzes the reasons and effects of mergers and acquisitions of enterprises in the logistics industry, and analyzes the mergers and acquisitions of Deppon Express by JD Logistics as a typical case. The article pointed out that in the context of globalization and digitalization, the logistics industry is undergoing profound changes, and the market competition is becoming increasingly fierce. As a leading enterprise in e-commerce logistics, JD Logistics aims to expand the market, enhance competitiveness, achieve resource integration and complementary advantages, and respond to market demand and policy support through the acquisition of Deppon Express. After the merger and acquisition, JD Logistics has significantly improved its logistics network coverage, supply chain service capacity, economic benefits, etc., while Deppon Express has significantly improved its corporate governance, business integration and service efficiency. This merger and acquisition not only enhances the strength of JD Logistics in the large logistics market, but also promotes the concentration of resources and the improvement of service quality in the logistics industry. At the same time, mergers and acquisitions also promote the development of green logistics and show the trend of the logistics industry to sustainable development. Through the case study of the merger and acquisition of Deppon Express by JD Logistics, this article provides valuable experience for the merger and acquisition of enterprises in the logistics industry, and looks forward to the steady development of the logistics industry in the future in resource integration and competitive cooperation.

Optimizing Multi-Echelon Delivery Routes for Perishable Goods with Time Constraints

As the logistics industry modernizes, living standards improve, and consumption patterns shift, the demand for fresh food continues to grow, making cold chain logistics for perishable goods a critical component in ensuring food quality and safety. However, the presence of both soft and hard time windows among demand nodes can complicate the single-network distribution of perishable goods. In response to these challenges, this paper proposes an optimization model for multi-distribution center perishable goods delivery, considering both one-echelon and two-echelon network joint distributions. The model aims to minimize total costs, including transportation, fixed, refrigeration, goods damage, and penalty costs, while measuring customer satisfaction by the start time of service at each demand node. A two-stage heuristic algorithm is designed to solve the model. In the first stage, an initial solution is constructed using a greedy approach based on the principles of the k-medoids clustering algorithm, which considers both spatial and temporal distances. In the second stage, the initial routing solution is optimized using a linear programming approach from the Ortools solver combined with an Improved Adaptive Large Neighborhood Search (IALNS) algorithm. The effectiveness of the proposed model and algorithm is validated through a case study analysis. The results demonstrate that the initial solutions obtained through the k-medoids clustering algorithm based on spatio-temporal distance improved the overall cost optimization by 1.85% and 4.74% compared to the other two algorithms. Among the three two-stage heuristic algorithms, the Ortools-IALNS proposed here showed enhancements in the overall cost optimization over the IALNS, with improvements of 3.24%, 1.12%, and 0.41%, respectively. The two-stage heuristic algorithm designed in this study also converged faster than the other two heuristic algorithms, with overall optimization improvements of 1.55% and 1.28%, further validating the superior performance of the proposed heuristic algorithm.

Enhancing supply chain resilience through artificial intelligence: Analyzing problem-solving approaches in logistics management

In an increasingly complex and unpredictable global economy, supply chain resilience has emerged as a critical priority for organizations striving to adapt to disruptions and maintain operational continuity. Artificial intelligence (AI) has proven to be a transformative force in addressing the challenges of logistics and supply chain management, offering innovative solutions for forecasting, decision-making, and operational efficiency. This research explores how AI-driven approaches can solve specific supply chain challenges, including demand variability, disruption management, inventory optimization, transportation inefficiencies, and supplier relationship management. The review proposes a comprehensive analysis of AI technologies such as predictive analytics, real-time monitoring systems, optimization algorithms, and autonomous systems, highlighting their contributions to enhancing supply chain resilience. By leveraging AI, organizations can improve their ability to predict disruptions, optimize resource allocation, and respond dynamically to real-time challenges. The research incorporates case studies and real-world applications to illustrate successful implementations of AI in logistics, as well as lessons learned from failed attempts. Additionally, the review examines the benefits of integrating AI into supply chain operations, such as improved risk management, enhanced decision-making, and increased adaptability to uncertainties. However, it also addresses the challenges of AI adoption, including high implementation costs, data privacy concerns, and organizational resistance to change. By presenting actionable insights and recommendations, this research aims to provide a roadmap for organizations seeking to harness AI to strengthen their supply chain resilience. The findings underscore the transformative potential of AI technologies in fostering robust, adaptive, and efficient supply chain systems capable of withstanding future uncertainties and disruptions. Keywords: Supply Chain, Artificial Intelligence, Logistics Management, Review.

Sustainability Practices and Compliance With the Sustainable Logistics Management Plan in Brazilian Federal Institutes

Objective: The objective of this study is to analyze the current stage of development, monitoring and dissemination of Sustainable Logistics Management Plans (PLS) published by Federal Institutes (IFS). Theoretical Framework: The excessive use of natural resources and the perception of environmental problems have led society to constantly discuss the negative impacts caused by the adopted production and consumption methods. In this way, the understanding of the relationship with the environment has been expanded, encouraging nations to engage in the search for sustainable development. In this movement, contemporary organizations have developed and developed strategies and actions in their daily lives in the search for sustainable practices. Method: The methodology adopted for this research comprises a descriptive survey, with a qualitative approach and a documentary type. Results and Discussion: The results obtained revealed that among the 38 Federal Institutes in Brazil, 17 have the PLS, which indicates that sustainability is still incipiently included in HEIs and that there is a lack of monitoring by government oversight bodies. The results also point to the need for greater transparency in the disclosure of sustainable practices, especially in relation to monitoring and evaluation reports of PLS. Research Implications: The practical and theoretical implications of this research are discussed, providing insights into how the results can be applied or influence practices in the field of sustainability. These implications may encompass administrative sectors. Originality/Value: This study contributes to the literature by highlighting the originality of the research, whether through an innovative approach, new findings or practical contributions. The relevance and value of this research are evidenced by the need for greater transparency in the disclosure of sustainable practices, especially in relation to monitoring and evaluation reports of PLS.

Correlation between admission blood glucose, fibrinogen, and slow blood flow during primary PCI for acute ST segment elevation myocardial infarction.

Coronary slow flow (CSF) is a common phenomenon of coronary microcirculation dysfunction, and is closely related to elevated blood glucose and fibrinogen (FIB) levels. However, whether immediate blood glucose and FIB levels affect coronary blood flow during primary percutaneous coronary intervention (PCI) remains unclear. To explore the correlation between admission blood glucose (ABG), fibrinogen (FIB) and slow blood flow during primary PCI for acute ST segment elevation myocardial infarction (STEMI). A total of 497 patients who underwent coronary angiography in the cardiology department of the 904th Hospital of the Joint Logistics Support Force from December 2018 to December 2022 due to STEMI were selected consecutively, and then were divided into two groups based on whether slow blood flow occurred during primary PCI: slow blood flow group (n = 117) and control group (n = 380). Detecting the ABG, FIB and other indicators of patients in each group, and using logistic regression analysis and receiver operating characteristic (ROC) curve to analyze independent risk factors for slow blood flow during primary PCI, and further evaluating the prognosis of patients. The levels of ABG and FIB in patients with slow blood flow were significantly higher than those in the control group (P < 0.05). The results of multivariate logistic regression analysis suggested that FIB and ABG were independent risk factors for slow blood flow during primary PCI (both P < 0.05). ROC curve analysis showed that ABG, FIB, and their combination all had predictive value for slow blood flow during primary PCI (all P < 0.05), and the area under the curve (AUC) of the combined indicator was higher than that of any single indicator, with statistical significance (P < 0.05). KM curve analysis suggested that the prognosis of patients in slow blood flow group were poor. Both elevated ABG and FIB could predict slow blood flow during primary PCI, and the diagnostic value of the combined indicator was superior to that of any single indicator, which could be used for the evaluation of slow blood flow during primary PCI, so as to evaluate the prognosis of patients with STEMI.

Analisis Strategi Pengembangan Bisnis Menggunakan Bisnis Model Kanvas pada Industri Logistik: Studi PT XYZ

This research analyzes and formulates the business strategy of PT XYZ using the Business Model Canvas to enhance and innovate in the logistics industry. The focus of the study is to identify logistics challenges, analyze the business model, develop business strategies, and provide policy recommendations to improve PT XYZ's competitiveness and operational efficiency. This research incorporates SWOT, PESTLE, and Porter’s Five Forces analyses in evaluating the nine building blocks of the Business Model Canvas. As a result, 26 strategies were developed, consisting of 9 SO strategies, 4 ST strategies, 6 WO strategies, and 6 WT strategies. Through focus group discussions and member checks, 9 priority strategies were identified and categorized into three approaches: short-term, medium-term, and long-term. The short-term strategies include offering competitive pricing packages, adopting innovative technology, and strengthening relationships with business partners. The medium-term strategies emphasize communication integration through social media and improving service quality. The long-term strategies focus on expanding the partner network and building innovative and sustainable partnerships. The implementation of the nine building blocks in the Business Model Canvas demonstrates PT XYZ's comprehensive adaptation to market dynamics and operational challenges, with the ultimate goal of strengthening its market position and supporting the company's sustainable growth.

ADAPTABLE DYNAMIC ROUTING SYSTEM IN URBAN TRANSPORT LOGISTICS PROBLEMS USING GIS DATA

To solve the problems of online route optimization in urban transport logistics, an adaptive dynamic routing system based on GIS data is proposed. Here, it is possible to simultaneously take into account the actual configuration of the urban road network (URN) and the real-time dynamics of traffic flows. Route optimization is performed on a weighted bidirectional graph for an asymmetric dynamic traveling salesman problem using a modified ant colony optimization algorithm. The system allows automatically updating the weights of the graph depending on the current changes in the characteristics traffic in the URN sections, obtained from GIS data, and fixing the optimal configuration of a partially completed route before updating the graph. To test the proposed system, the simulation of dynamic routing processes was conducted in real-time, using the delivery of goods to Żabka grocery stores in Warsaw as an example. The results indicate the proposed method’s feasibility for solving practical urban transport logistics management problems under complex traffic.

E-commerce Logistics Performance and Resilience: The Influence of Inter-Organizational Trust and Organizational Flexibility

This study investigates the role of inter-organizational information and communication technology (Inter-OICT) in enhancing e-commerce logistics performance (LP) and resilience (LR). Grounded in organizational information processing theory (OIPT) and relational exchange theory (RET), the research outlines how Inter-OICT influences LP and LR through the mediating role of inter-organizational trust (Inter-OT) and the moderating effect of organizational flexibility (OFLX). A cross-sectional survey of logistics managers facilitated the testing of these relationships. The findings indicate that Inter-OICT significantly boosts both LP and LR, with Inter-OT serving as a crucial mediator by enhancing the effectiveness of ICT deployments. Additionally, organizational flexibility was found to strengthen the impact of Inter-OICT on both performance metrics. This research underlines the importance of strategic ICT integration and flexible organizational practices in maximizing logistics efficacy and resilience, presenting novel insights into the dynamics of technology adoption in the logistics sector.

MIDDLE EASTERNERS AS NEOCOLONIAL NOMADS: NEOCOLONIAL DETERRITORIALIZATOIN OF MIDDLE EASTERNERS IN MOHSIN HAMID’S THE RELUCTANT FUNDAMENTALIST AND EXIT WEST

Neocolonialism amounts to the contemporary practices conducted by imperialists to exploit underdeveloped countries after decolonization. Initially, neocolonialists performed indirect methods, such as economic and logistic support to the monarchs or governments and radical groups that were heavily influenced by political Islam, in the Middle East, but it has then evolved into an extreme form through direct military interventions after the 9/11 terrorist attacks. The study argues that those practices have dislocated Middle Easterners, and their dislocations are associated with the unfixity of identities. Grounding on Deleuze and Guattarian nomadology and identifying their dislocations as neocolonial deterritorialization, the study examines the changes Middle Easterners experience due to their exposure to the factors in neocolonial spaces and in new territories. In this regard, the study aims to analyse the deterritorializing impacts of neocolonialism on Middle Easterner immigrants The Reluctant Fundamentalist and Exit West.

EXAMINING THE FACTORS THAT AFFECT INTERNSHIP STUDENT TEACHERS' USE OF TECHNOLOGY: A STUDY OF UNIVERSITAS NEGERI MAKASSAR

This study investigates the factors influencing internship student teachers' use of technology at Universitas Negeri Makassar. The research aims to identify key determinants that shape their preferences and attitudes toward integrating technology into teaching practices. Data were collected and analyzed using Confirmatory Factor Analysis (CFA), revealing several critical factors with varying levels of influence based on their loading factors. The strongest predictors of technology use included Student Interest and Motivation (0.81), Perceived Value (0.79), and Self-Efficacy (0.76). These highlight the importance of student engagement, personal confidence, and the perceived utility of technology in driving adoption. Other significant factors were Resources and Infrastructure (0.72), Alignment with Curriculum Goals (0.64), and Administrative Support (0.63), underscoring the role of institutional and logistical support. Moderate contributions were noted from Societal and Cultural Influences (0.61), Government Policies and Mandates (0.57), and Professional Development Opportunities (0.54). These indicate external and professional contexts shaping technology integration. Meanwhile, Innovative Teaching Practices (0.53) and Professional Development (0.43) demonstrated lower impacts, suggesting room for improvement in training initiatives.

The Impact of E-commerce on the Sales of Agricultural and Sideline Products

This study explores the impact of e-commerce on the sales of agricultural and sideline products, examining how digital platforms have transformed traditional marketing and distribution channels in the agricultural sector. The research underscores the significance of e-commerce as a pivotal tool for enhancing market accessibility and efficiency for farmers and producers, particularly in rural and remote areas. By leveraging e-commerce, agricultural producers can bypass traditional intermediaries, thereby reducing transaction costs and increasing profit margins. The study employs a comprehensive analysis of market trends, consumer behavior, and sales data to evaluate the effectiveness of e-commerce platforms. It identifies key factors that influence the successful integration of e-commerce in agricultural sales, including infrastructure development, digital literacy, and logistical support. The findings reveal a substantial increase in the reach and scale of market access for agricultural products, alongside improved price competitiveness. By facilitating direct interaction between producers and consumers, e-commerce contributes to greater transparency and trust in the supply chain. The conclusion emphasizes the transformative potential of e-commerce in reshaping the agricultural market landscape, advocating for policy support and investment in digital infrastructure to further enhance its benefits. This research provides valuable insights for stakeholders seeking to optimize the use of e-commerce in agricultural sales, highlighting the need for strategic planning and collaboration among policymakers, industry players, and technology providers.

Service recovery and customer loyalty in the logistics industry: the role of justice perceptions and switching costs

Service recovery and customer loyalty in the logistics industry: the role of justice perceptions and switching costs